Keywords: Neurodegeneration, Modelling, Disease progression
S-EBM generalises the event-based model (EBM) of disease progression for simultaneous events. In synthetic data, S-EBM can tell the difference between simultaneous and non-simultaneous events under a range of experimental conditions. In comparison to conventional EBM, S-EBM avoids artificial serial orderings, permitting more accurate and parsimonious descriptions of disease progression. When applied to real Alzheimer’s disease biomarker data, S-EBM estimates a sequence containing neurologically plausible simultaneous events which more closely explain the data than conventional EBM. S-EBM may be applied to recover novel patterns of disease progression thereby informing our understanding of disease evolution.
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